ResectVol: A tool to automatically segment and characterize lacunas in brain images

Autor(es)

Casseb, Raphael F. ^[1] ; de Campos, Brunno M. ^[1] ; Morita-Sherman, Marcia ^[2] ; Morsi, Amr ^[2] ; Kondylis, Efstathios ^[2] ; Bingaman, William E. ^[2] ; Jones, Stephen E. ^[2] ; Jehi, Lara ^[2] ; Cendes, Fernando ^[1]

Afiliação do(s) autor(es)

^[1] Univ Estadual Campinas, Dept Neurol, Neuroimaging Lab, Cidade Univ Zeferino Vaz, Campinas, SP - Brazil

^[2] Cleveland Clin Fdn, Epilepsy Ctr, 9500 Euclid Ave, Cleveland, OH 44195 - USA

• Tipo de documento: Artigo Científico
• Fonte: EPILEPSIA OPEN; v. 6, n. 4 OCT 2021.
• Citações Web of Science: 0

Resumo

Objective To assess and validate the performance of a new tool developed for segmenting and characterizing lacunas in postoperative MR images of epilepsy patients. Methods A MATLAB-based pipeline was implemented using SPM12 to produce the 3D mask of the surgical lacuna and estimate its volume. To validate its performance, we compared the manual and automatic lacuna segmentations obtained from 51 MRI scans of epilepsy patients who underwent temporal lobe resections. Results The code is consolidated as a tool named ResectVol, which can be run via a graphical user interface or command line. The automatic and manual segmentation comparison resulted in a median Dice similarity coefficient of 0.77 (interquartile range: 0.71-0.81). Significance Epilepsy surgery is the treatment of choice for pharmacoresistant focal epilepsies, and despite the extensive literature on the subject, we still cannot predict surgical outcomes accurately. As the volume and location of the resected tissue are fundamentally relevant to this prediction, researchers commonly perform a manual segmentation of the lacuna, which presents human bias and does not provide detailed information about the structures removed. In this study, we introduce ResectVol, a user-friendly, fully automatic tool to accomplish these tasks. This capability enables more advanced analytical techniques applied to surgical outcomes prediction, such as machine-learning algorithms, by facilitating coregistration of the resected area and preoperative findings with other imaging modalities such as PET, SPECT, and functional MRI ResectVol is freely available at . (AU)

• Processo FAPESP: 20/00019-7 - Análise avançada da ressonância magnética na epilepsia para prever o resultado do tratamento cirúrgico: uma abordagem de aprendizado de máquina
• Beneficiário: Raphael Fernandes Casseb
• Modalidade de apoio: Bolsas no Brasil - Pós-Doutorado